Exploiting Geometric Frustration in Coupled von Mises Trusses to Program Multifunctional Mechanical Metamaterials
Bibliographic record
Abstract
Multistable mechanical metamaterials are an emerging class of materials whose intricate internal structure can be engineered to program mechanical properties and promote reversible transitions between multiple stable states of energy. In this work, the design of a mechanical metamaterial based on an assembly of bistable von Mises trusses is presented. It is shown that coupling two von Mises trusses induces geometric frustration, which leads to an asymmetry between the stable states. Then the von Mises trusses are combined to build a unit cell that can change effective stiffness in compression when switching states. Based on a semi‐analytical model, the stiffness variation is characterized as a function of the geometric parameters and three possible scenarios are highlighted: 1) increased, 2) decreased, or 3) constant stiffness between the stable states. To validate the concept, the multistable metamaterials out of polylactic acid and thermoplastic polyurethane via fused filament fabrication are fabricated, and their mechanical response is evaluated by measuring experimentally the effective stiffness in both stable states under compression. This unit cell also features modularity, enabling reversible assembly and post‐fabrication tunability. Finally, a range of applications are explored, including sandwich panels capable of changing their compressive and bending stiffness as well as their surface morphology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".